DAE was one of the earliest deep learning based unsupervised learning techniques for SER [1]}. This was followed by the use of sparse AE for feature transfer [2]} and for SER on spontaneous data sets [3]}. Furthermore, end-to-end representation learning for affect recognition from speech was proposed and showed performance comparable to existing methods [4]}. In recent years, techniques like variational and adversarial AEs and adversarial variational Bayes have been exploited to learn the latent representations of speech emotions with input features ranging from the raw signals to hand crafted features [5]}, [6]}, [7]}, [8]}.
